Updated: 2020-08-13 07:24:28 PDT

Original version created 2020-05-03. See below for revision history

Intro


The spread of the SARS-COV-19 viral disease defies description in terms of a single statistic. To be informed about personal risk we need to know more than how many people have been sick at a national level or even state level, we need information about how many people are currently sick in our communicty and how the number of sick people is changing is changing at a state and even county level. It can be hard to find this information.

This analysis seeks to fill partially that gap. It includes:
1. Several national pictures of disease trends to enable a “large pattern” view of how disease has and is evolving a on country-wide scale.
2. A per capita analysis of disease spread.
3. A more granular analysis of regions, states, and counties to shed light on local disease pattern evolution.
4. Details of the time evolution of growth statistics.


This computed document is constantly evolving, so please “refresh” for the latest updates. If you have suggestions or comments please reach out on twitter @WinstonOnData or facebook.

National Maps

There are plenty of online maps. I’ve deprecated a few of the ones I’ve computer since they are no longer relevant to the analysis of disease trends. They are published:
- here.

Cases and Deaths per Capita

This chart reveals a more interesting pattern of disease spread. I haven’t found one of these online.
Groups of cities (e.g. Chicago, Indianapolis, and Detroit) and paths between connected communities are clearly visible.

Reproduction and Control

\(R_e\) is a measure of disease growth. For recovery to begin disease growth must turn from positive to negative (i.e. from \(log_2\)(\(R_e\)) > 0 to \(log_2\)(\(R_e\)) < 0).

After achieving negative growth growth, the next phase of recovery is maintaining consistently lower levels of disease. Control can be measured as a ratio of current disease levels to maximum disease levels. If disease levels are currently at a maximum, control is 0 %.

\[ control = 100 \times (1 - \frac{active \space disease}{max(active \space disease)} ) \% \]

State Level Data


County Level Data


state R_e cases daily_cases
South Dakota 1.14 9641 100
North Dakota 1.13 7970 147
West Virginia 1.11 8061 140
Idaho 1.10 26435 519
Illinois 1.09 199764 1845
Indiana 1.09 78655 980
Kansas 1.09 32740 449
Kentucky 1.09 38461 696
Arkansas 1.08 49921 813
Vermont 1.08 1462 5
Virginia 1.08 82118 914
California 1.07 592970 8327
Georgia 1.07 207958 3572
Montana 1.06 5228 120
Rhode Island 1.05 18218 93
Texas 1.05 534621 8325
Minnesota 1.04 62700 731
Missouri 1.04 56298 1083
Oregon 1.04 22220 328
Iowa 1.03 50155 471
Nebraska 1.03 29201 286
Wisconsin 1.03 63199 874
Alabama 1.02 106160 1592
Michigan 1.02 98827 725
Ohio 1.02 104644 1214
Tennessee 1.02 125488 2033
Washington 1.02 67659 766
Oklahoma 1.01 46247 869
Pennsylvania 1.01 126081 821
New York 1.00 427481 647
Maryland 0.99 98542 822
Nevada 0.99 59149 926
New Jersey 0.99 187090 366
North Carolina 0.99 140755 1582
Florida 0.98 555343 7306
Massachusetts 0.98 121740 372
New Hampshire 0.98 6903 27
Utah 0.98 45447 427
Colorado 0.97 52168 451
Louisiana 0.97 135716 1590
Mississippi 0.97 70416 1031
South Carolina 0.97 104269 1266
Delaware 0.96 15578 82
New Mexico 0.94 22988 206
Wyoming 0.93 3125 35
Connecticut 0.88 50546 92
Maine 0.87 4082 14
Arizona 0.85 191899 1388

National Statistics

Total & Active Cases, and Deaths

These trend charts show the national disease statistics. The raw data are shown. since these showdaily trends that are systematically related ot the M-F work week, possibly due to reporting delays, numbers showsn

Mortality Trend

\(R_e\) Trend

National effective reproduction rate

Distribution of \(R_e\) Values

Howver, there is a wiude dirstubtion of \(R_e\) across regions and counties. The distributions in the graph below looks roughly symmetrical because the x-scale is logarithmic.

Distribution of Baseline Control

Similarly for disease control, when take at the county level, there is a wide distribution of Baseline Control.

Regional Snapshots

Regional snapshots reveal the highly nuanced behavior of disease spread. Each snaphot includes multiple states and selected counties.

How to read the charts

There are four components:
1. State Maps show the number of active cases and with the Reproduction rate encoded as color.
2. State Graphs State-wide trend graphs.
3. Severity Ranking These is a table of counties where the highest number of new cases are expected. Severity is a compounded function \(f(R, cases(t))\). This is useful for finding new (often unexpected) “hot spots.” Added per capita rates.
4. County Graphs encode the R-value in the active number of cases. R is the Reproduction Rate.

(NOTE: R < 1 implies a shrinking number of active cases, R > 1 implies a growing number of active cases. For R = 1, active cases are stable. ).


Washington and Oregon

WA
county ST case rank severity R_e cases cases/100k daily cases
King WA 1 1 1.0 17086 790 158
Spokane WA 5 2 1.1 4643 930 87
Pierce WA 3 3 1.0 6542 760 104
Grant WA 9 4 1.1 1649 1740 35
Snohomish WA 4 5 1.0 6381 810 58
Clark WA 8 6 1.1 2160 460 32
Chelan WA 10 7 1.1 1416 1870 31
Yakima WA 2 8 0.9 11076 4440 50
Franklin WA 7 11 0.9 3732 4120 29
Benton WA 6 14 0.9 3998 2060 31
OR
county ST case rank severity R_e cases cases/100k daily cases
Multnomah OR 1 1 1.0 5099 640 68
Washington OR 2 2 1.0 3216 550 42
Marion OR 3 3 1.0 3022 900 38
Malheur OR 6 4 1.2 837 2750 18
Yamhill OR 10 5 1.1 495 480 15
Umatilla OR 4 6 0.9 2398 3120 36
Clackamas OR 5 7 1.0 1599 390 20
Jackson OR 9 8 1.1 510 240 14
Lane OR 8 11 1.0 612 170 10
Deschutes OR 7 15 1.0 633 350 10
## Warning: Removed 1 rows containing missing values (geom_col).

California

CA
county ST case rank severity R_e cases cases/100k daily cases
Los Angeles CA 1 1 1.0 215560 2130 2396
Riverside CA 2 2 1.1 43502 1830 583
Merced CA 20 3 1.3 6024 2240 203
Sacramento CA 11 4 1.2 12176 810 242
Stanislaus CA 13 5 1.2 10871 2020 210
Santa Clara CA 10 6 1.2 13022 680 283
Alameda CA 8 7 1.2 13783 840 243
Orange CA 3 8 1.1 41514 1310 454
San Bernardino CA 4 9 1.0 37893 1770 520
San Joaquin CA 9 11 1.1 13615 1860 211
Kern CA 6 12 1.0 25615 2900 526
San Diego CA 5 13 1.0 33805 1020 393
Fresno CA 7 14 1.0 18582 1900 342

Four Corners

AZ
county ST case rank severity R_e cases cases/100k daily cases
Maricopa AZ 1 1 0.8 129216 3040 903
Pima AZ 2 2 1.0 18561 1820 214
Cochise AZ 11 3 1.1 1735 1370 22
Yuma AZ 3 4 0.8 11806 5680 68
Yavapai AZ 10 5 1.0 2098 930 28
Mohave AZ 6 6 0.9 3298 1600 27
Pinal AZ 4 7 0.8 8607 2050 44
Apache AZ 7 9 0.9 3232 4520 17
Coconino AZ 8 10 0.9 3141 2240 15
Navajo AZ 5 11 0.8 5428 4990 15
Santa Cruz AZ 9 13 0.8 2694 5780 8
CO
county ST case rank severity R_e cases cases/100k daily cases
El Paso CO 4 1 1.0 5355 780 68
Adams CO 3 2 1.0 6675 1340 63
Denver CO 1 3 0.9 10444 1510 68
Jefferson CO 5 4 1.0 4310 760 38
Larimer CO 9 5 1.1 1616 480 23
Arapahoe CO 2 6 0.9 7472 1170 48
Mesa CO 17 7 1.2 334 220 8
Weld CO 6 8 1.0 3775 1280 22
Boulder CO 7 9 1.0 2136 670 20
Douglas CO 8 12 0.9 1783 540 14
UT
county ST case rank severity R_e cases cases/100k daily cases
Salt Lake UT 1 1 1.0 21219 1890 176
Utah UT 2 2 1.0 9054 1530 112
Weber UT 4 3 1.0 2881 1160 30
Davis UT 3 4 0.9 3323 980 32
Washington UT 5 5 0.9 2574 1600 24
Cache UT 6 6 1.0 1960 1600 13
Wasatch UT 10 7 1.2 577 1890 5
Tooele UT 9 11 0.8 598 920 5
San Juan UT 8 13 0.8 660 4320 3
Summit UT 7 14 0.9 719 1770 2
NM
county ST case rank severity R_e cases cases/100k daily cases
Lea NM 7 1 1.2 857 1220 23
Doña Ana NM 4 2 1.1 2564 1190 34
Chaves NM 11 3 1.2 497 760 16
Eddy NM 13 4 1.2 324 560 8
Bernalillo NM 1 5 0.9 5286 780 42
Curry NM 10 6 1.0 586 1170 12
Valencia NM 12 7 1.0 468 620 8
Santa Fe NM 9 8 0.9 676 450 9
San Juan NM 3 9 0.9 3071 2410 7
Cibola NM 8 10 0.7 744 2760 14
McKinley NM 2 11 0.8 4081 5600 8
Sandoval NM 5 12 0.8 1157 820 7
Otero NM 6 18 0.6 1110 1690 2

Mid-Atlantic

NJ
county ST case rank severity R_e cases cases/100k daily cases
Union NJ 6 1 1.3 16911 3060 17
Bergen NJ 1 2 1.0 21152 2270 38
Hudson NJ 3 3 1.1 19899 2980 23
Passaic NJ 5 4 1.0 17877 3550 26
Gloucester NJ 16 5 1.0 3318 1140 22
Essex NJ 2 6 1.0 20054 2530 29
Camden NJ 9 7 1.0 8714 1720 32
Middlesex NJ 4 9 1.0 18199 2200 27
Monmouth NJ 8 10 0.9 10488 1680 28
Ocean NJ 7 13 0.9 10744 1820 23
PA
county ST case rank severity R_e cases cases/100k daily cases
Union PA 39 1 1.4 270 600 13
Philadelphia PA 1 2 1.0 31697 2010 121
York PA 13 3 1.2 2657 600 36
Fayette PA 25 4 1.3 562 420 19
Northumberland PA 28 5 1.4 498 540 11
Delaware PA 3 6 1.0 9482 1680 67
Allegheny PA 4 7 0.9 9180 750 93
Lancaster PA 6 8 1.0 6046 1120 45
Montgomery PA 2 9 1.0 10241 1250 41
Berks PA 7 11 1.0 5463 1310 28
Chester PA 8 17 0.9 5244 1010 31
Bucks PA 5 18 0.9 7298 1170 32
Lehigh PA 9 21 1.0 5018 1380 18
MD
county ST case rank severity R_e cases cases/100k daily cases
Baltimore city MD 4 1 1.0 13077 2130 164
Prince George’s MD 1 2 1.0 24623 2720 150
Baltimore MD 3 3 1.0 13707 1660 155
Montgomery MD 2 4 1.0 18704 1800 97
Anne Arundel MD 5 5 1.0 7563 1330 64
Howard MD 6 6 1.0 3964 1260 36
Charles MD 8 7 1.0 2092 1330 22
Harford MD 9 8 1.0 2056 820 26
Frederick MD 7 14 0.9 3120 1260 12
VA
county ST case rank severity R_e cases cases/100k daily cases
Floyd VA 76 1 2.0 85 540 8
Wise VA 59 2 1.7 166 430 11
Scott VA 71 3 1.6 113 510 8
Mecklenburg VA 28 4 1.5 456 1480 16
Russell VA 66 5 1.6 140 510 10
Pittsylvania VA 25 6 1.3 520 840 21
Fairfax VA 1 7 1.1 16613 1450 83
Prince William VA 2 8 1.0 9649 2110 72
Virginia Beach city VA 4 9 1.0 5293 1180 101
Chesterfield VA 5 11 1.0 4496 1320 48
Loudoun VA 3 13 1.1 5368 1390 33
Norfolk city VA 7 14 0.9 3901 1590 63
Henrico VA 6 17 1.0 3994 1230 40
Arlington VA 8 22 1.1 3127 1350 20
Newport News city VA 9 26 0.9 1907 1060 25
WV
county ST case rank severity R_e cases cases/100k daily cases
Logan WV 7 1 1.4 273 810 17
Raleigh WV 8 2 1.2 272 360 10
Cabell WV 4 3 1.1 426 450 10
Kanawha WV 1 4 1.0 988 530 18
Grant WV 19 5 1.2 137 1180 7
Mercer WV 11 6 1.1 219 360 8
Boone WV 24 7 1.2 109 480 3
Wood WV 9 9 1.3 256 300 2
Berkeley WV 3 13 1.0 712 630 6
Monongalia WV 2 19 0.7 960 910 4
Ohio WV 6 22 0.8 276 650 2
Jefferson WV 5 23 0.9 298 530 1
DE
county ST case rank severity R_e cases cases/100k daily cases
New Castle DE 1 1 0.9 7354 1320 47
Sussex DE 2 2 1.0 5901 2690 23
Kent DE 3 3 1.0 2324 1330 12

Deep South

AL
county ST case rank severity R_e cases cases/100k daily cases
Clarke AL 36 1 1.8 814 3340 43
Mobile AL 2 2 1.1 10916 2630 241
Jefferson AL 1 3 1.0 13983 2120 217
Washington AL 50 4 1.5 445 2670 16
Montgomery AL 3 5 1.1 7113 3130 88
Jackson AL 28 6 1.2 1109 2130 33
Talladega AL 21 7 1.1 1378 1710 36
Tuscaloosa AL 5 9 1.0 4488 2180 52
Baldwin AL 6 10 0.9 3842 1850 63
Madison AL 4 12 0.9 5724 1600 69
Shelby AL 7 13 1.0 3643 1720 48
Marshall AL 8 14 1.0 3344 3510 37
Lee AL 9 16 1.0 2951 1850 33
MS
county ST case rank severity R_e cases cases/100k daily cases
Harrison MS 3 1 1.1 2685 1330 60
Lee MS 10 2 1.1 1572 1850 40
Stone MS 77 3 1.4 219 1190 8
DeSoto MS 2 4 1.0 3838 2180 61
Union MS 38 5 1.2 692 2440 20
Tishomingo MS 51 6 1.2 456 2340 17
Jackson MS 5 7 0.9 2449 1720 49
Hinds MS 1 11 0.8 5866 2430 64
Washington MS 9 12 1.0 1750 3720 28
Forrest MS 8 14 1.0 1884 2490 27
Jones MS 7 21 0.9 1971 2880 22
Madison MS 4 31 0.8 2511 2430 22
Rankin MS 6 40 0.8 2375 1570 24
LA
county ST case rank severity R_e cases cases/100k daily cases
Lafayette LA 4 1 1.0 7994 3330 142
East Baton Rouge LA 2 2 1.0 12659 2850 162
St. Landry LA 15 3 1.1 2900 3480 67
Jefferson LA 1 4 0.9 15645 3590 118
St. Tammany LA 7 5 1.0 5407 2140 68
St. Martin LA 19 6 1.1 1752 3260 25
Ouachita LA 8 7 1.0 5019 3220 55
Tangipahoa LA 9 8 1.0 3608 2760 47
Orleans LA 3 12 0.9 10892 2800 52
Caddo LA 6 14 0.9 6870 2770 61
Calcasieu LA 5 29 0.7 7081 3540 65

FL and GA

FL
county ST case rank severity R_e cases cases/100k daily cases
Baker FL 49 1 2.0 1020 3670 94
Dixie FL 57 2 1.8 589 3580 43
Union FL 65 3 1.8 406 2660 25
Miami-Dade FL 1 4 0.9 139250 5130 1792
Taylor FL 46 5 1.5 1167 5280 92
Suwannee FL 38 6 1.5 1637 3730 60
Escambia FL 12 7 1.2 9947 3190 209
Broward FL 2 9 0.9 65107 3410 774
Palm Beach FL 3 12 0.9 38372 2650 424
Hillsborough FL 4 14 1.0 33611 2440 354
Polk FL 9 17 1.0 14916 2230 203
Duval FL 6 18 1.0 24137 2610 247
Orange FL 5 19 0.9 32586 2470 283
Pinellas FL 7 21 0.9 18381 1920 165
Lee FL 8 29 0.9 16946 2360 124
GA
county ST case rank severity R_e cases cases/100k daily cases
Bleckley GA 116 1 1.7 254 1990 16
Cobb GA 4 2 1.1 14376 1930 289
Gwinnett GA 2 3 1.1 20810 2310 336
Fulton GA 1 4 1.0 21294 2080 334
DeKalb GA 3 5 1.1 14535 1960 218
Cherokee GA 11 6 1.2 3699 1530 93
Richmond GA 9 7 1.1 4708 2340 118
Chatham GA 6 9 1.0 6055 2110 108
Hall GA 5 14 1.0 6335 3230 86
Clayton GA 7 16 1.0 5250 1880 76
Muscogee GA 8 22 1.0 4911 2500 63

Texas & Oklahoma

TX
county ST case rank severity R_e cases cases/100k daily cases
Bee TX 48 1 1.7 1328 4060 107
Brooks TX 160 2 2.1 133 1850 8
Fort Bend TX 10 3 1.3 10480 1420 338
Harris TX 1 4 1.0 90078 1960 1454
Nueces TX 9 5 1.2 16056 4450 403
Williamson TX 16 6 1.4 7002 1330 144
Cameron TX 7 7 1.1 18821 4460 694
Tarrant TX 4 8 1.1 35433 1750 627
Dallas TX 2 13 1.0 56226 2170 514
El Paso TX 8 14 1.1 17104 2040 254
Hidalgo TX 6 15 1.0 20833 2450 321
Travis TX 5 21 1.0 23508 1950 226
Bexar TX 3 39 0.7 44074 2290 263
OK
county ST case rank severity R_e cases cases/100k daily cases
Pittsburg OK 25 1 1.6 406 910 28
Tulsa OK 2 2 1.0 11098 1730 210
Oklahoma OK 1 3 1.0 11174 1430 193
Le Flore OK 27 4 1.2 396 790 20
Garfield OK 15 5 1.2 510 820 17
Rogers OK 5 6 1.0 1062 1170 28
Cleveland OK 3 7 0.9 3188 1150 54
Wagoner OK 7 8 1.0 922 1180 21
Canadian OK 4 12 0.9 1289 940 24
Comanche OK 9 27 0.9 855 700 9
Texas OK 6 43 1.0 1062 5030 3
McCurtain OK 8 44 0.9 874 2650 5

Michigan & Wisconsin

MI
county ST case rank severity R_e cases cases/100k daily cases
Muskegon MI 13 1 1.6 1299 750 23
Macomb MI 3 2 1.1 11032 1270 124
Oakland MI 2 3 1.0 15858 1270 114
Wayne MI 1 4 0.9 28603 1620 131
Kent MI 4 5 0.9 7681 1190 50
Menominee MI 42 6 1.3 154 660 7
Bay MI 21 7 1.2 653 620 11
Saginaw MI 8 8 1.1 2076 1080 23
Washtenaw MI 6 9 1.0 3118 850 20
Ottawa MI 9 13 1.0 1883 660 15
Genesee MI 5 15 0.9 3718 910 21
Jackson MI 7 42 0.7 2456 1550 5
WI
county ST case rank severity R_e cases cases/100k daily cases
Sawyer WI 51 1 1.6 81 490 6
Oneida WI 40 2 1.6 153 430 9
Milwaukee WI 1 3 0.9 21643 2270 191
Waukesha WI 3 4 1.0 4506 1130 92
Lafayette WI 41 5 1.5 149 890 5
Washington WI 11 6 1.1 1154 860 30
Dane WI 2 7 1.0 4659 880 48
Brown WI 4 13 1.0 4367 1680 38
Racine WI 5 16 1.0 3629 1860 42
Outagamie WI 9 17 1.1 1324 720 24
Kenosha WI 6 27 0.9 2746 1630 26
Walworth WI 8 31 0.9 1390 1350 17
Rock WI 7 39 0.9 1594 990 9

Minnesota, North Dakota, and South Dakota

MN
county ST case rank severity R_e cases cases/100k daily cases
Hennepin MN 1 1 1.0 19872 1610 216
Ramsey MN 2 2 1.1 7830 1450 102
McLeod MN 32 3 1.5 206 580 9
Dakota MN 3 4 1.1 4608 1100 73
St. Louis MN 18 5 1.3 604 300 22
Anoka MN 4 6 1.1 3822 1100 52
Washington MN 6 7 1.1 2213 870 35
Scott MN 9 8 1.1 1635 1140 29
Olmsted MN 7 9 1.1 1782 1160 17
Stearns MN 5 18 0.9 2926 1870 11
Nobles MN 8 29 1.0 1771 8110 3
SD
county ST case rank severity R_e cases cases/100k daily cases
Minnehaha SD 1 1 1.0 4498 2410 30
Lincoln SD 3 2 1.0 671 1220 12
Codington SD 8 3 1.3 139 500 2
Yankton SD 10 4 1.3 120 530 2
Brookings SD 7 5 1.3 142 410 3
Brown SD 5 6 1.1 454 1170 5
Pennington SD 2 7 1.0 912 830 8
Clay SD 9 11 1.1 133 960 2
Union SD 6 13 1.0 221 1460 2
Beadle SD 4 14 1.0 594 3230 1
ND
county ST case rank severity R_e cases cases/100k daily cases
Rolette ND 15 1 1.9 88 600 9
Sioux ND 14 2 1.9 90 2040 5
McLean ND 17 3 1.7 79 820 6
Morton ND 4 4 1.3 407 1330 16
Burleigh ND 2 5 1.1 1279 1360 35
Stark ND 5 6 1.3 300 970 12
Cass ND 1 7 1.0 3082 1770 16
Ward ND 7 9 1.1 244 350 7
Grand Forks ND 3 10 1.1 704 1000 8
Williams ND 6 11 1.0 286 840 5
Mountrail ND 9 12 1.2 139 1370 3
Benson ND 8 17 0.5 147 2130 3

Connecticut, Massachusetts, and Rhode Island

CT
county ST case rank severity R_e cases cases/100k daily cases
New Haven CT 2 1 1.0 13246 1540 19
Fairfield CT 1 2 0.9 18118 1920 31
New London CT 5 3 1.1 1465 540 6
Hartford CT 3 4 0.8 12868 1440 21
Windham CT 8 5 1.0 749 640 5
Middlesex CT 6 6 1.0 1408 860 2
Tolland CT 7 7 0.7 1071 710 4
Litchfield CT 4 8 0.8 1622 890 2
MA
county ST case rank severity R_e cases cases/100k daily cases
Suffolk MA 2 1 1.0 22056 2790 73
Middlesex MA 1 2 1.0 26625 1670 77
Essex MA 3 3 1.0 17990 2300 63
Norfolk MA 5 4 0.9 10741 1540 41
Bristol MA 6 5 1.0 9443 1690 31
Worcester MA 4 6 0.9 13729 1670 34
Plymouth MA 7 7 1.0 9299 1820 18
Hampden MA 8 8 1.0 7660 1630 21
Barnstable MA 9 10 0.8 1817 850 6
RI
county ST case rank severity R_e cases cases/100k daily cases
Providence RI 1 1 1.1 15352 2420 78
Kent RI 2 2 1.0 1528 930 10
Washington RI 3 3 1.0 615 490 2
Newport RI 4 4 1.0 402 480 2
Bristol RI 5 5 0.9 321 660 2

New York

NY
county ST case rank severity R_e cases cases/100k daily cases
New York City NY 1 1 1.0 233387 2760 295
Erie NY 7 2 1.0 9018 980 45
Suffolk NY 2 3 1.0 43973 2960 62
Nassau NY 3 4 1.0 43793 3230 46
Westchester NY 4 5 1.0 36305 3750 32
Monroe NY 8 6 1.0 5033 680 27
Onondaga NY 10 7 1.0 3615 780 13
Rockland NY 5 8 1.1 13963 4310 8
Orange NY 6 11 1.0 11206 2960 10
Dutchess NY 9 12 0.9 4641 1580 12

Vermont, New Hampshire, and Maine

VT
county ST case rank severity R_e cases cases/100k daily cases
Bennington VT 5 1 1.3 89 250 1
Rutland VT 4 2 1.1 102 170 1
Chittenden VT 1 3 1.0 734 450 1
Windham VT 3 4 1.3 103 240 0
Franklin VT 2 5 0.8 119 240 0
ME
county ST case rank severity R_e cases cases/100k daily cases
Cumberland ME 1 1 0.9 2100 720 5
Androscoggin ME 3 2 1.0 568 530 2
York ME 2 3 0.9 683 340 3
Kennebec ME 4 4 0.8 173 140 1
Penobscot ME 5 5 0.8 154 100 1
NH
county ST case rank severity R_e cases cases/100k daily cases
Rockingham NH 2 1 1.0 1708 560 7
Hillsborough NH 1 2 0.9 3884 940 12
Strafford NH 4 3 1.1 364 280 3
Cheshire NH 7 4 1.1 102 130 1
Merrimack NH 3 5 1.0 467 310 1
Belknap NH 5 6 1.0 118 190 1
Carroll NH 8 7 0.8 96 200 1
Grafton NH 6 8 0.6 104 120 0

Carolinas

SC
county ST case rank severity R_e cases cases/100k daily cases
Richland SC 3 1 1.0 9135 2240 118
Charleston SC 1 2 0.9 12634 3200 100
Aiken SC 15 3 1.1 2014 1210 47
Greenville SC 2 4 0.9 11191 2250 94
Florence SC 10 5 1.0 3635 2620 66
Beaufort SC 6 6 1.0 4325 2370 73
Spartanburg SC 8 7 1.0 4227 1400 48
York SC 9 8 1.0 3718 1440 53
Horry SC 4 9 0.9 8790 2740 66
Berkeley SC 7 12 1.0 4322 2070 45
Lexington SC 5 18 0.9 5114 1790 45
NC
county ST case rank severity R_e cases cases/100k daily cases
Mecklenburg NC 1 1 0.9 22833 2170 192
Wake NC 2 2 1.0 12469 1190 133
Northampton NC 67 3 1.4 349 1730 9
Wilkes NC 43 4 1.3 848 1240 13
Cumberland NC 8 5 1.0 3248 980 55
Guilford NC 4 6 1.0 5809 1110 61
Forsyth NC 5 7 1.0 5394 1450 48
Union NC 9 8 1.0 3228 1420 43
Durham NC 3 13 1.0 6300 2060 43
Gaston NC 6 15 1.0 3441 1590 42
Johnston NC 7 17 0.9 3425 1790 40

North-Rockies

MT
county ST case rank severity R_e cases cases/100k daily cases
Yellowstone MT 1 1 1.1 1377 870 33
Silver Bow MT 9 2 1.3 106 300 6
Flathead MT 4 3 1.1 373 380 13
Big Horn MT 3 4 1.0 476 3560 15
Missoula MT 5 5 1.1 354 310 9
Glacier MT 13 6 1.2 79 580 2
Gallatin MT 2 7 0.9 985 940 10
Lewis and Clark MT 8 8 1.0 170 250 4
Cascade MT 7 10 0.8 177 220 3
Lake MT 6 12 0.8 188 630 2
WY
county ST case rank severity R_e cases cases/100k daily cases
Washakie WY 12 1 1.5 81 1000 4
Carbon WY 9 2 1.0 108 700 3
Campbell WY 8 3 1.1 129 270 1
Sheridan WY 13 4 1.0 78 260 2
Laramie WY 1 5 0.8 514 530 4
Natrona WY 6 6 0.9 240 300 2
Fremont WY 2 7 0.9 510 1270 3
Uinta WY 4 8 0.9 284 1380 2
Sweetwater WY 5 9 0.8 269 610 2
Teton WY 3 10 0.6 388 1680 3
Park WY 7 11 0.7 138 470 2
ID
county ST case rank severity R_e cases cases/100k daily cases
Shoshone ID 22 1 1.9 124 990 8
Bonneville ID 5 2 1.3 1237 1100 60
Ada ID 1 3 1.1 9452 2120 143
Canyon ID 2 4 1.1 6153 2900 128
Twin Falls ID 4 5 1.1 1477 1770 27
Kootenai ID 3 6 1.0 1917 1250 34
Jefferson ID 14 7 1.2 228 820 10
Jerome ID 8 14 1.1 500 2130 8
Cassia ID 7 18 0.9 544 2300 7
Minidoka ID 9 21 0.9 498 2420 6
Blaine ID 6 25 1.0 579 2630 1

## Warning in FUN(X[[i]], ...): NaNs produced

Midwest

OH
county ST case rank severity R_e cases cases/100k daily cases
Franklin OH 1 1 1.0 18952 1490 187
Madison OH 37 2 1.5 426 970 14
Cuyahoga OH 2 3 1.0 13917 1110 129
Hamilton OH 3 4 1.0 9871 1220 78
Lucas OH 4 5 0.9 5613 1300 81
Lawrence OH 45 6 1.3 311 510 11
Summit OH 6 7 1.0 3691 680 45
Montgomery OH 5 8 1.0 4530 850 55
Butler OH 7 10 1.0 3038 800 37
Mahoning OH 9 17 1.1 2618 1130 22
Marion OH 8 54 1.0 2946 4510 7
IL
county ST case rank severity R_e cases cases/100k daily cases
Cook IL 1 1 1.0 113214 2170 675
Logan IL 53 2 1.7 145 500 9
Tazewell IL 22 3 1.4 613 460 30
LaSalle IL 17 4 1.4 846 770 38
Jersey IL 58 5 1.6 121 550 7
Jefferson IL 35 6 1.5 309 810 15
Will IL 5 7 1.1 9509 1380 93
DuPage IL 3 8 1.1 12535 1350 108
Madison IL 9 9 1.1 2779 1050 66
Lake IL 2 11 1.0 12954 1840 95
Kane IL 4 13 1.1 10030 1890 79
St. Clair IL 6 15 1.0 4614 1750 71
McHenry IL 8 22 1.1 3336 1080 38
Winnebago IL 7 46 0.9 3829 1340 15
IN
county ST case rank severity R_e cases cases/100k daily cases
Sullivan IN 70 1 1.7 143 690 9
Marion IN 1 2 1.0 16436 1740 164
Vigo IN 25 3 1.4 725 670 31
Lake IN 2 4 1.1 7803 1600 72
Allen IN 4 5 1.1 4051 1090 47
St. Joseph IN 5 6 1.1 3649 1360 56
Putnam IN 43 7 1.4 320 850 10
Hamilton IN 6 9 1.1 2918 920 44
Vanderburgh IN 7 12 1.0 2084 1150 40
Elkhart IN 3 13 1.0 5048 2480 38
Hendricks IN 8 15 1.1 1967 1220 21
Johnson IN 9 21 1.1 1834 1210 16

Tennessee and Kentucky

TN
county ST case rank severity R_e cases cases/100k daily cases
Weakley TN 45 1 1.5 538 1600 37
Shelby TN 1 2 1.0 24533 2620 312
Johnson TN 59 3 1.4 345 1940 26
Davidson TN 2 4 1.0 23589 3450 204
Overton TN 74 5 1.5 215 980 10
Knox TN 5 6 1.0 5240 1150 120
Madison TN 20 7 1.2 1244 1270 46
Hamilton TN 4 8 1.0 6411 1790 83
Rutherford TN 3 15 0.9 6780 2210 74
Williamson TN 6 22 1.0 3676 1680 44
Wilson TN 8 25 1.0 2385 1800 33
Montgomery TN 9 27 1.0 2044 1040 36
Sumner TN 7 32 1.0 3535 1970 36
KY
county ST case rank severity R_e cases cases/100k daily cases
Jefferson KY 1 1 1.2 8828 1150 189
Fayette KY 2 2 1.1 4152 1300 92
Madison KY 14 3 1.4 562 630 21
Hardin KY 8 4 1.3 693 640 20
Calloway KY 29 5 1.3 261 670 9
Breckinridge KY 74 6 1.4 77 380 3
Nelson KY 32 7 1.3 245 540 6
Warren KY 3 9 1.0 2721 2150 29
Kenton KY 4 12 1.0 1481 900 18
Christian KY 9 13 1.1 684 950 12
Shelby KY 7 15 1.1 789 1690 9
Boone KY 5 23 1.0 1132 880 12
Daviess KY 6 29 1.0 792 790 9

Missouri and Arkansas

MO
county ST case rank severity R_e cases cases/100k daily cases
St. Louis MO 1 1 1.0 15754 1580 261
Greene MO 6 2 1.2 1702 590 48
Jackson MO 4 3 1.0 4342 630 99
Taney MO 14 4 1.2 687 1260 32
St. Louis city MO 2 5 1.0 5449 1750 86
St. Francois MO 21 6 1.4 423 640 14
Jefferson MO 5 7 1.1 1952 870 51
St. Charles MO 3 11 1.0 4383 1120 72
Boone MO 7 15 1.1 1491 840 28
Clay MO 9 19 1.0 1105 460 22
Jasper MO 8 38 0.9 1300 1090 10
AR
county ST case rank severity R_e cases cases/100k daily cases
Jackson AR 54 1 2.0 126 730 10
Poinsett AR 32 2 1.6 314 1310 20
Logan AR 33 3 1.5 312 1430 19
Pulaski AR 2 4 1.1 5831 1480 98
Sebastian AR 4 5 1.1 2356 1850 62
Mississippi AR 15 6 1.2 1117 2610 39
Independence AR 22 7 1.2 607 1630 29
Craighead AR 7 9 1.1 1442 1360 33
Jefferson AR 5 11 1.1 1624 2310 30
Hot Spring AR 6 12 1.3 1560 4650 11
Crittenden AR 8 19 1.0 1418 2890 20
Benton AR 3 22 0.9 4870 1880 33
Washington AR 1 24 0.9 6408 2800 36
Pope AR 9 34 0.9 1371 2150 16

Conclusions

It’s in control some places, but not all places. And many places are completely out-of-control.

Stay Safe!
Be Diligent!
…and PLEASE WEAR A MASK



Built with R Version 4.0.2
This document took 508.7 seconds to compute.
2020-08-13 07:32:56

version history

Today is 2020-08-13.
85 days ago: Multiple states.
77 days ago: \(R_e\) computation.
74 days ago: created color coding for \(R_e\) plots.
69 days ago: Reduced \(t_d\) from 14 to 12 days. 14 was the upper range of what most people are using. Wanted slightly higher bandwidth.
69 days ago: “persistence” time evolution.
62 days ago: “In control” mapping.
62 days ago: “Severity” tables to county analysis. Severity is computed from the number of new cases expected at current \(R_e\) for 6 days in the future. It does not trend \(R_e\), which could be a future enhancement.
54 days ago: Added census API functionality to compute per capita infection rates. Reduced spline spar = 0.65.
49 days ago: Added Per Capita US Map.
47 days ago: Deprecated national map.
43 days ago: added state “Hot 10” analysis.
38 days ago: cleaned up county analysis to show cases and actual data. Moved “Hot 10” analysis to separate web page. Moved “Hot 10” here.
36 days ago: added per capita disease and mortaility to state-level analysis.
24 days ago: changed to county boundaries on national map for per capita disease.
19 days ago: corrected factor of two error in death trend data.
15 days ago: removed “contained and uncontained” analysis, replacing it with county level control map.
10 days ago: added county level “baseline control” and \(R-e\) maps.
6 days ago: fixed normalization error on total disease stats plot.

Appendix: Methods

Disease data are sourced from the NYTimes Github Repo. Population data are sourced from the US Census census.gov

Case growth is assumed to follow a linear-partial differential equation. This type of model is useful in populations where there is still very low immunity and high susceptibility.

\[\frac{\partial}{\partial t} cases(t, t_d) = a \times cases(t, t_d) \] \(cases(t)\) is the number of active cases at \(t\) dependent on recent history, \(t_d\). The constant \(a\) and has units of \(time^{-1}\) and is typically computed on a daily basis

Solution results are often expressed in terms of the Effective Reproduction Rate \(R_e\), where \[a \space = \space ln(R_e).\]

\(R_e\) has a simple interpretation; when \(R_e \space > \space 1\) the number of \(cases(t)\) increases (exponentially) while when \(R_e \space < \space 1\) the number of \(cases(t)\) decreases.

Practically, computing \(a\) can be extremely complicated, depending on how functionally it is related to history \(t_d\). And guessing functional forms can be as much art as science. To avoid that, let’s keep things simple…

Assuming a straight-forward flat time of latent infection \(t_d\) = 12 days, with \[f(t) = \int_{t - t_d}^{t}cases(t')\; dt' ,\] \(R_e\) reduces to a simple computation

\[R_e(t) = \frac{cases(t)}{\int_{t - t_d}^{t}cases(t')\; dt'} \times t_d .\]

Typical range of \(t_d\) range \(7 \geq t_d \geq 14\). The only other numerical treatment is, in order to reduce noise the data, I smooth case data with a reticulated spline to compute derivatives.


DISCLAIMER: Results are for entertainment purposes only. Please consult local authorities for official data and forecasts.